OpenRaiser/Intern-Atlas huggingface dataset:
A Modularized Design Approach for GelSight Family of Vision-based Tactile Sensors
Venue: arXiv cs.RO 2025
Year: 2025

GelSight family of vision-based tactile sensors has proven to be effective for multiple robot perception and manipulation tasks. These sensors are based on an internal optical system and an embedded camera to capture the deformation of the soft sensor surface, inferring the high-resolution geometry of the objects in contact. However, customizing the sensors for different robot hands requires a tedious trial-and-error process to re-design the optical system. In this paper, we formulate the GelSight sensor design process as a systematic and objective-driven design problem and perform the design optimization with a physically accurate optical simulation. The method is based on modularizing and parameterizing the sensor's optical components and designing four generalizable objective functions to evaluate the sensor. We implement the method with an interactive and easy-to-use toolbox called OptiSense Studio. With the toolbox, non-sensor experts can quickly optimize their sensor design in both forward and inverse ways following our predefined modules and steps. We demonstrate our system with four different GelSight sensors by quickly optimizing their initial design in simulation and transferring it to the real sensors.
Interference-Aware PMI selection for MIMO systems in an O-RAN scenario
Venue: arXiv cs.NI 2025
Year: 2025

The optimization of Precoding Matrix Indicators (PMIs) is crucial for enhancing the performance of 5G networks, particularly in dense deployments where inter-cell interference is a significant challenge. Some approaches have leveraged AI/ML techniques for beamforming and beam selection, however, these methods often overlook the multi-objective nature of PMI selection, which requires balancing spectral efficiency (SE) and interference reduction. This paper proposes an interference-aware PMI selection method using an Advantage Actor-Critic (A2C) reinforcement learning model, designed for deployment within an O-RAN framework as an xApp. The proposed model prioritizes user equipment (UE) based on a novel strategy and adjusts PMI values accordingly, with interference management and efficient resource utilization. Experimental results in an O-RAN environment demonstrate the approach's effectiveness in improving network performance metrics, including SE and interference mitigation.
PPO-EPO: Energy and Performance Optimization for O-RAN Using Reinforcement Learning
Venue: arXiv cs.NI 2025
Year: 2025

Energy consumption in mobile communication networks has become a significant challenge due to its direct impact on Capital Expenditure (CAPEX) and Operational Expenditure (OPEX). The introduction of Open RAN (O-RAN) enables telecommunication providers to leverage network intelligence to optimize energy efficiency while maintaining Quality of Service (QoS). One promising approach involves traffic-aware cell shutdown strategies, where underutilized cells are selectively deactivated without compromising overall network performance. However, achieving this balance requires precise traffic steering mechanisms that account for throughput performance, power efficiency, and network interference constraints. This work proposes a reinforcement learning (RL) model based on the Proximal Policy Optimization (PPO) algorithm to optimize traffic steering and energy efficiency. The objective is to maximize energy efficiency and performance gains while strategically shutting down underutilized cells. The proposed RL model learns adaptive policies to make optimal shutdown decisions by considering throughput degradation constraints, interference thresholds, and PRB utilization balance. Experimental validation using TeraVM Viavi RIC tester data demonstrates that our method significantly improves the network's energy efficiency and downlink throughput.
Advancing Video Anomaly Detection: A Bi-Directional Hybrid Framework for Enhanced Single- and Multi-Task Approaches
Venue: arXiv cs.CV 2025
Year: 2025

Despite the prevailing transition from single-task to multi-task approaches in video anomaly detection, we observe that many adopt sub-optimal frameworks for individual proxy tasks. Motivated by this, we contend that optimizing single-task frameworks can advance both single- and multi-task approaches. Accordingly, we leverage middle-frame prediction as the primary proxy task, and introduce an effective hybrid framework designed to generate accurate predictions for normal frames and flawed predictions for abnormal frames. This hybrid framework is built upon a bi-directional structure that seamlessly integrates both vision transformers and ConvLSTMs. Specifically, we utilize this bi-directional structure to fully analyze the temporal dimension by predicting frames in both forward and backward directions, significantly boosting the detection stability. Given the transformer's capacity to model long-range contextual dependencies, we develop a convolutional temporal transformer that efficiently associates feature maps from all context frames to generate attention-based predictions for target frames. Furthermore, we devise a layer-interactive ConvLSTM bridge that facilitates the smooth flow of low-level features across layers and time-steps, thereby strengthening predictions with fine details. Anomalies are eventually identified by scrutinizing the discrepancies between target frames and their corresponding predictions. Several experiments conducted on public benchmarks affirm the efficacy of our hybrid framework, whether used as a standalone single-task approach or integrated as a branch in a multi-task approach. These experiments also underscore the advantages of merging vision transformers and ConvLSTMs for video anomaly detection.
SWE-Synth: Synthesizing Verifiable Bug-Fix Data to Enable Large Language Models in Resolving Real-World Bugs
Venue: arXiv cs.SE 2025
Year: 2025

Large language models (LLMs) are transforming automated program repair (APR) through agent-based approaches that localize bugs, generate patches, and verify fixes. However, the lack of high-quality, scalable training datasets, especially those with verifiable outputs and intermediate reasoning traces-limits progress, particularly for open-source models. In this work, we present SWE-Synth, a framework for synthesizing realistic, verifiable, and process-aware bug-fix datasets at the repository level. SWE-Synth leverages LLM agents to simulate debugging workflows, producing not only bug-fix pairs but also test cases and structured repair trajectories. Compared to manually curated datasets, our method scales with minimal human effort while preserving contextual richness and correctness. Experiments show that models trained on SWE-Synth outperform those trained on real-world datasets by 2.3% on SWE-Bench Lite. Our results highlight the potential of synthetic, agent-generated data to advance the state of the art in APR and software engineering automation.
Establishing Workload Identity for Zero Trust CI/CD: From Secrets to SPIFFE-Based Authentication
Venue: arXiv cs.CR 2025
Year: 2025

CI/CD systems have become privileged automation agents in modern infrastructure, but their identity is still based on secrets or temporary credentials passed between systems. In enterprise environments, these platforms are centralized and shared across teams, often with broad cloud permissions and limited isolation. These conditions introduce risk, especially in the era of supply chain attacks, where implicit trust and static credentials leave systems exposed. This paper describes the shift from static credentials to OpenID Connect (OIDC) federation, and introduces SPIFFE (Secure Production Identity Framework for Everyone) as a runtime-issued, platform-neutral identity model for non-human actors. SPIFFE decouples identity from infrastructure, enabling strong, portable authentication across job runners and deployed workloads. We show how SPIFFE identities support policy alignment, workload attestation, and mutual authentication. The paper concludes by outlining next steps in enabling policy-based access, forming the basis of a broader Zero Trust architecture for CI/CD.
PLANET: A Collection of Benchmarks for Evaluating LLMs' Planning Capabilities
Venue: arXiv cs.AI 2025
Year: 2025

Planning is central to agents and agentic AI. The ability to plan, e.g., creating travel itineraries within a budget, holds immense potential in both scientific and commercial contexts. Moreover, optimal plans tend to require fewer resources compared to ad-hoc methods. To date, a comprehensive understanding of existing planning benchmarks appears to be lacking. Without it, comparing planning algorithms' performance across domains or selecting suitable algorithms for new scenarios remains challenging. In this paper, we examine a range of planning benchmarks to identify commonly used testbeds for algorithm development and highlight potential gaps. These benchmarks are categorized into embodied environments, web navigation, scheduling, games and puzzles, and everyday task automation. Our study recommends the most appropriate benchmarks for various algorithms and offers insights to guide future benchmark development.
Optimal Linear MAP Decoding of Convolutional Codes
Venue: arXiv cs.IT 2025
Year: 2025

In this paper, we propose a linear representation of BCJR maximum a posteriori probability (MAP) decoding of a rate 1/2 convolutional code (CC), referred to as the linear MAP decoding (LMAP). We discover that the MAP forward and backward decoding can be implemented by the corresponding dual soft input and soft output (SISO) encoders using shift registers. The bidrectional MAP decoding output can be obtained by combining the contents of respective forward and backward dual encoders. Represented using simple shift-registers, LMAP decoder maps naturally to hardware registers and thus can be easily implemented. Simulation results demonstrate that the LMAP decoding achieves the same performance as the BCJR MAP decoding, but has a significantly reduced decoding delay. For the block length 64, the CC of the memory length 14 with LMAP decoding surpasses the random coding union (RCU) bound by approximately 0.5 dB at a BLER of $10^{-3}$, and closely approaches both the normal approximation (NA) and meta-converse (MC) bounds.
A Survey on Small Sample Imbalance Problem: Metrics, Feature Analysis, and Solutions
Venue: arXiv cs.LG 2025
Year: 2025

The small sample imbalance (S&I) problem is a major challenge in machine learning and data analysis. It is characterized by a small number of samples and an imbalanced class distribution, which leads to poor model performance. In addition, indistinct inter-class feature distributions further complicate classification tasks. Existing methods often rely on algorithmic heuristics without sufficiently analyzing the underlying data characteristics. We argue that a detailed analysis from the data perspective is essential before developing an appropriate solution. Therefore, this paper proposes a systematic analytical framework for the S\&I problem. We first summarize imbalance metrics and complexity analysis methods, highlighting the need for interpretable benchmarks to characterize S&I problems. Second, we review recent solutions for conventional, complexity-based, and extreme S&I problems, revealing methodological differences in handling various data distributions. Our summary finds that resampling remains a widely adopted solution. However, we conduct experiments on binary and multiclass datasets, revealing that classifier performance differences significantly exceed the improvements achieved through resampling. Finally, this paper highlights open questions and discusses future trends.
vApps: Verifiable Applications at Internet Scale
Venue: arXiv cs.CR 2025
Year: 2025

Blockchain technology promises a decentralized, trustless, and interoperable infrastructure. However, widespread adoption remains hindered by issues such as limited scalability, high transaction costs, and the complexity of maintaining coherent verification logic across different blockchain layers. This paper introduces Verifiable Applications (vApps), a novel development framework designed to streamline the creation and deployment of verifiable blockchain computing applications. vApps offer a unified Rust-based Domain-Specific Language (DSL) within a comprehensive SDK, featuring modular abstractions for verification, proof generation, and inter-chain connectivity. This eases the developer's burden in securing diverse software components, allowing them to focus on application logic. The DSL also ensures that applications can automatically take advantage of specialized precompiles and hardware acceleration to achieve consistently high performance with minimal developer effort, as demonstrated by benchmark results for zero-knowledge virtual machines (zkVMs). Experiments show that native Rust execution eliminates interpretation overhead, delivering up to an 197x cycle count improvement compared to EVM-based approaches. Precompiled circuits can accelerate the proof by more than 95%, while GPU acceleration increases throughput by up to 30x and recursion compresses the proof size by up to 230x, enabling succinct and efficient verification. The framework also supports seamless integration with the Web2 and Web3 systems, enabling developers to focus solely on their application logic. Through modular architecture, robust security guarantees, and composability, vApps pave the way toward a trust-minimized and verifiable Internet-scale application environment.
What Lurks Within? Concept Auditing for Shared Diffusion Models at Scale
Venue: arXiv cs.LG 2025
Year: 2025

Diffusion models (DMs) have revolutionized text-to-image generation, enabling the creation of highly realistic and customized images from text prompts. With the rise of parameter-efficient fine-tuning (PEFT) techniques, users can now customize powerful pre-trained models using minimal computational resources. However, the widespread sharing of fine-tuned DMs on open platforms raises growing ethical and legal concerns, as these models may inadvertently or deliberately generate sensitive or unauthorized content. Despite increasing regulatory attention on generative AI, there are currently no practical tools for systematically auditing these models before deployment. In this paper, we address the problem of concept auditing: determining whether a fine-tuned DM has learned to generate a specific target concept. Existing approaches typically rely on prompt-based input crafting and output-based image classification but they suffer from critical limitations, including prompt uncertainty, concept drift, and poor scalability. To overcome these challenges, we introduce Prompt-Agnostic Image-Free Auditing (PAIA), a novel, model-centric concept auditing framework. By treating the DM as the object of inspection, PAIA enables direct analysis of internal model behavior, bypassing the need for optimized prompts or generated images. We evaluate PAIA on 320 controlled models trained with curated concept datasets and 771 real-world community models sourced from a public DM sharing platform. Evaluation results show that PAIA achieves over 90% detection accuracy while reducing auditing time by 18 - 40X compared to existing baselines. To our knowledge, PAIA is the first scalable and practical solution for pre-deployment concept auditing of diffusion models, providing a practical foundation for safer and more transparent diffusion model sharing.
An Enhanced Dual-Currency VCG Auction Mechanism for Resource Allocation in IoV: A Value of Information Perspective
Venue: arXiv cs.GT 2025
Year: 2025

The Internet of Vehicles (IoV) is undergoing a transformative evolution, enabled by advancements in future 6G network technologies, to support intelligent, highly reliable, and low-latency vehicular services. However, the enhanced capabilities of loV have heightened the demands for efficient network resource allocation while simultaneously giving rise to diverse vehicular service requirements. For network service providers (NSPs), meeting the customized resource-slicing requirements of vehicle service providers (VSPs) while maximizing social welfare has become a significant challenge. This paper proposes an innovative solution by integrating a mean-field multi-agent reinforcement learning (MFMARL) framework with an enhanced Vickrey-Clarke-Groves (VCG) auction mechanism to address the problem of social welfare maximization under the condition of unknown VSP utility functions. The core of this solution is introducing the ``value of information" as a novel monetary metric to estimate the expected benefits of VSPs, thereby ensuring the effective execution of the VCG auction mechanism. MFMARL is employed to optimize resource allocation for social welfare maximization while adapting to the intelligent and dynamic requirements of IoV. The proposed enhanced VCG auction mechanism not only protects the privacy of VSPs but also reduces the likelihood of collusion among VSPs, and it is theoretically proven to be dominant-strategy incentive compatible (DSIC). The simulation results demonstrate that, compared to the VCG mechanism implemented using quantization methods, the proposed mechanism exhibits significant advantages in convergence speed, social welfare maximization, and resistance to collusion, providing new insights into resource allocation in intelligent 6G networks.
Exploring $\ell_0$ Sparsification for Inference-free Sparse Retrievers
Venue: arXiv cs.IR 2025
Year: 2025

With increasing demands for efficiency, information retrieval has developed a branch of sparse retrieval, further advancing towards inference-free retrieval where the documents are encoded during indexing time and there is no model-inference for queries. Existing sparse retrieval models rely on FLOPS regularization for sparsification, while this mechanism was originally designed for Siamese encoders, it is considered to be suboptimal in inference-free scenarios which is asymmetric. Previous attempts to adapt FLOPS for inference-free scenarios have been limited to rule-based methods, leaving the potential of sparsification approaches for inference-free retrieval models largely unexplored. In this paper, we explore $\ell_0$ inspired sparsification manner for inference-free retrievers. Through comprehensive out-of-domain evaluation on the BEIR benchmark, our method achieves state-of-the-art performance among inference-free sparse retrieval models and is comparable to leading Siamese sparse retrieval models. Furthermore, we provide insights into the trade-off between retrieval effectiveness and computational efficiency, demonstrating practical value for real-world applications.
APIRAT: Integrating Multi-source API Knowledge for Enhanced Code Translation with LLMs
Venue: arXiv cs.SE 2025
Year: 2025

Code translation is an essential task in software migration, multilingual development, and system refactoring. Recent advancements in large language models (LLMs) have demonstrated significant potential in this task. However, prior studies have highlighted that LLMs often struggle with domain-specific code, particularly in resolving cross-lingual API mappings. To tackle this challenge, we propose APIRAT, a novel code translation method that integrates multi-source API knowledge. APIRAT employs three API knowledge augmentation techniques, including API sequence retrieval, API sequence back-translation, and API mapping, to guide LLMs to translating code, ensuring both the correct structure of API sequences and the accurate usage of individual APIs. Extensive experiments on two public datasets, CodeNet and AVATAR, indicate that APIRAT significantly surpasses existing LLM-based methods, achieving improvements in computational accuracy ranging from 4% to 15.1%. Additionally, our evaluation across different LLMs showcases the generalizability of APIRAT. An ablation study further confirms the individual contributions of each API knowledge component, underscoring the effectiveness of our approach.
Collaborative Enhancement Network for Low-quality Multi-spectral Vehicle Re-identification
Venue: arXiv cs.CV 2025
Year: 2025

The performance of multi-spectral vehicle Re-identification (ReID) is significantly degraded when some important discriminative cues in visible, near infrared and thermal infrared spectra are lost. Existing methods generate or enhance missing details in low-quality spectra data using the high-quality one, generally called the primary spectrum, but how to justify the primary spectrum is a challenging problem. In addition, when the quality of the primary spectrum is low, the enhancement effect would be greatly degraded, thus limiting the performance of multi-spectral vehicle ReID. To address these problems, we propose the Collaborative Enhancement Network (CoEN), which generates a high-quality proxy from all spectra data and leverages it to supervise the selection of primary spectrum and enhance all spectra features in a collaborative manner, for robust multi-spectral vehicle ReID. First, to integrate the rich cues from all spectra data, we design the Proxy Generator (PG) to progressively aggregate multi-spectral features. Second, we design the Dynamic Quality Sort Module (DQSM), which sorts all spectra data by measuring their correlations with the proxy, to accurately select the primary spectra with the highest correlation. Finally, we design the Collaborative Enhancement Module (CEM) to effectively compensate for missing contents of all spectra by collaborating the primary spectra and the proxy, thereby mitigating the impact of low-quality primary spectra. Extensive experiments on three benchmark datasets are conducted to validate the efficacy of the proposed approach against other multi-spectral vehicle ReID methods. The codes will be released at https://github.com/yongqisun/CoEN.
Towards Fuzzing Zero-Knowledge Proof Circuits (Short Paper)
Venue: arXiv cs.CR 2025
Year: 2025

Zero-knowledge proofs (ZKPs) have evolved from a theoretical cryptographic concept into a powerful tool for implementing privacy-preserving and verifiable applications without requiring trust assumptions. Despite significant progress in the field, implementing and using ZKPs via \emph{ZKP circuits} remains challenging, leading to numerous bugs that affect ZKP circuits in practice, and \emph{fuzzing} remains largely unexplored as a method to detect bugs in ZKP circuits. We discuss the unique challenges of applying fuzzing to ZKP circuits, examine the oracle problem and its potential solutions, and propose techniques for input generation and test harness construction. We demonstrate that fuzzing can be effective in this domain by implementing a fuzzer for \texttt{zk-regex}, a cornerstone library in modern ZKP applications. In our case study, we discovered \textit{$10$} new bugs that have been confirmed by the developers.